Chicago Crimes

Table of Content:

1. Introduction
2. Objective
3. Questions

Introduction

Crime analysis has become one of the most important topics in the field of data science, since the availability of crime data sets, in addition to the need of analyzing the reasons of those crime activities and the prediction of future crimes, according to the past criminal records. A lot of online crime datasets are available nowadays, like the city of Atlanta Crime 2009-2017 dataset, city of Baltimore Crime 2011-2016 dataset and many other datasets that can be found here.

But in this project we are going to work on the Crimes in Chicago Dataset.

Objective

The objective of the activity is to answer some questions regarding criminology. Moreover, answering these questions and reaching a well-established analysis, will give us a chance to use these approaches with crimes in other cities also. The aim is to explore the dataset and find key points that can help in detecting the major areas of crime, factors behind these crimes and safe spots around the city. Moreover, we explore and answer more questions regarding performance indicators of the police in the city and the correlation between socioeconomic factors and crimes in the city.

Questions

The questions we have answered:

1. What're the Top 10 Crimes by Type?
2. WordCloud by All the Crime Types
3. What're the Top 4 Crimes for the Most Dangerous Locations in Chicago?
4. What're the Top Crimes Related to Domestic Violence?
5. What's the Relationship between Police Performance and Total Crimes Over Years?
6. What's the Distribution of the Money-Driven Crimes by Month and Weekday?
7. What're the Most Dangerous Locations in Chicago?

Back to Top

What're the Top 10 Crimes by Type?

We found that Theft, Battery and Criminal Damage are the top crimes in Chicago.

top_10_types = crimes.primary_type.value_counts()[:10] # Sort values in ascending order

x = top_10_types.index # Get x labels and order

fig, ax = plt.subplots(figsize=figsize) # Create figure and axes with default figure size

dark_reds = get_dark_colors(101, 6, 'Reds', 10)
dark_reds.reverse()

sb_barplot = sb.barplot(ax = ax, x = top_10_types, y = x, order = x, 
                        palette = dark_reds, edgecolor=None, linewidth=0) # Crea bar plot
sb.despine(left=True, bottom=True) # Remove plot borders
for crime, p in zip(x.values, sb_barplot.patches): # Add annotation for each patch
    sb_barplot.annotate(text       = crime + ' (' + format(int(p.get_width()), ',d') + ')', 
                        xy         = (p.get_width() - 40000, p.get_y() + p.get_height() * 0.57), 
                        ha         = 'left',
                        va         = 'center', 
                        xytext     = (40, 0), 
                        textcoords = 'offset points',
                        color = 'black',
                        size = 15)

plt.xticks(fontsize=15, color = 'black') # Increase x ticks labels font size
plt.xticks([]), plt.yticks([]) # Remove x and y ticks

plt.xlabel(''), plt.ylabel('') # Remove x and y labels

plt.title('Top 10 Crime Types in Chicago by Count', y = title_y, fontsize = title_size, color='black'); # Add descriptive title

plt.tight_layout() # Improve appearance

plt.savefig('src/top_10_types.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file

WordCloud by All the Crime Types

Now let's see all the crimes in a WordCloud..

dard_reds = get_dark_colors(100, 7, 'Reds', 12)
dard_reds = LinearSegmentedColormap.from_list('mycmap', dard_reds)

wordcloud2 = WordCloud(width = 3000,
                       height = 2000,
                       background_color ='white',
                       random_state=1,
                       collocations=False,
                       min_font_size=50,
                       colormap='rocket').generate_from_frequencies(crimes_counts)

plt.figure(figsize=figsize)
plt.imshow(wordcloud2) 
plt.axis("off")

plt.title('WordCloud by All the Crime Types', y = 1.16, fontsize=40, color = '#c60000') # Set title
plt.tight_layout(pad = 0) 
  
plt.savefig('src/word_cloud.png', bbox_inches='tight', facecolor='white', pad_inches=1); # Save as png file

What're the Top 4 Crimes for the Most Dangerous Locations in Chicago?

Theft crimes are active in streets and parking garages, Narcotic crimes are active in sidewalks and alleys, while Battery crimes are active in schools, public buildings, residences and apartments.

def set_4_plots(start_edge, end_edge, save_num):
    # Create figure with 3*2 subplots
    fig, axes_sub = plt.subplots(2, 2, figsize=[20, 9]) # Create figure and axes with default figure size
    
    fig.subplots_adjust(hspace=0.6, wspace=0.05)

    # Flatten axes array
    axes = axes_sub.flatten()

    # Add common title
    fig.suptitle('The top 4 Crimes for The Most Dangerous Locations in Chicago', 
                 fontsize=title_size, y = title_y+0.05, color = 'black', x = 0.58)
     
    color_dic = get_my_colors('Set2', 10, top_primary_types)
        
    for ax_n, place in enumerate(danger_plcaes.sort_values()[start_edge : end_edge]):
        ax = axes[ax_n] # Current axis
        
        # Subsetting the data to the current crime
        place_df = top_danger_plcaes[top_danger_plcaes['location_description'] == place] 
        types = place_df.primary_type.values
        color_palette = {}
        for t in types:
            color_palette[t] = color_dic[t]
        
        # Add barplot
        barplot = sb.barplot(ax = ax, x = list(place_df.primary_type), y = place_df['count'], 
                             palette = color_palette, edgecolor=None, linewidth=0)
        
        # Add title for the current plot
        ax.set_title(place, fontsize = 20, color = 'black', y = 1.11)
        
        # Remove labels from axes
        ax.set_xlabel(''), ax.set_ylabel(''), ax.set_yticklabels(''), ax.set_xticklabels('')
        
        ax.set_facecolor('xkcd:white') # Setting face color to white
        
        sb.despine(left=True, bottom=True) # Remove plot borders
        
        for p in barplot.patches: # Add annotation for each patch
            barplot.annotate(text       = format(int(p.get_height()), ',d'),
                             xy         = (p.get_x() + p.get_width() / 2., p.get_height()),
                             ha         = 'center', va = 'center',
                             xytext     = (0, 9),
                             textcoords = 'offset points',
                             color='black',
                             size = 15)
    
    add_custome_legend(color_dic=color_dic, fontsize=23, x=1.12, y=0.768, fig=fig, handle='patch')
    #plt.tight_layout() # Improve appearance

    plt.savefig(f'src/top_danger_plcaes{save_num}.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file
    
set_4_plots(0, 4, 1)

set_4_plots(4, 9, 2)

What're the Top Crimes Related to Domestic Violence?

Battery crimes are the most common crimes related to Domestic Violence.

top_domestic_crimes = crimes[crimes['domestic']].reset_index(drop=True).primary_type.value_counts()[:6].sort_values()
x = list(top_domestic_crimes.index.values) # Get x labels and order

fig, ax = plt.subplots(figsize=figsize) # Create figure and axes with default figure size

dark_blues = get_dark_colors(50, 4, 'Blues', 6)

sb_barplot = sb.barplot(ax = ax, x = x, y = top_domestic_crimes, order = x, 
                        palette = dark_blues, edgecolor=None, linewidth=0) # Crea bar plot
sb.despine(left=True, bottom=True) # Remove plot borders

for p in sb_barplot.patches: # Add annotation for each patch
    sb_barplot.annotate(text       = format(int(p.get_height()), ',d'),
                        xy         = (p.get_x() + p.get_width() / 2., p.get_height()),
                        ha         = 'center', va = 'center',
                        xytext     = (0, 9),
                        textcoords = 'offset points',
                        color='black',
                        size = 15)

plt.xticks(fontsize=15, color = 'black') # Increase x ticks labels font size
plt.yticks([]), plt.xlabel(''), plt.ylabel('') # Remove ticks and label

plt.title('Top Domestic Crimes by Count', y = title_y, fontsize = title_size); # Add descriptive title

plt.tight_layout() # Improve appearance

plt.savefig('src/top_domestic_crimes.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file

What's the Relationship between Police Performance and Total Crimes Over Years?

We found a decrease in police performance from 2014 to 2016 despite a decrease in the total number of crimes in those years.

fig, ax = plt.subplots(1, 1, figsize=figsize) # Create figure and axes with default figure size
ax_color, ax2_color = '#990011FF', '#1D65A6'

# Add common title
fig.suptitle('Police Performance VS. Total Crimes Over Years', fontsize = title_size, y = title_y, color = 'black')

sb.lineplot(ax = ax, x = list(success_ratio.index), y = success_ratio, ci=0, color=ax_color) 

x_ticks = ax.get_xticks()
x_ticks[0] = 2000.5
x_ticks_labels = [str(int(x)) for x in x_ticks]
x_ticks_labels[0], x_ticks_labels[-1] = '', ''
ax.set_xticks(x_ticks), ax.set_xticklabels(x_ticks_labels, fontsize=15, color = 'black')

set_y_ticks(ax=ax, up_edge=30, step=10, str_suff='%', color='black')

ax2 = ax.twinx()
sb.lineplot(ax = ax2, x = list(tot_crimes_year.index), y = tot_crimes_year, ci=0, color=ax2_color) 
set_y_ticks(ax=ax2, up_edge=200000, step=100000, str_suff='k', color='black')

ax.set_xlabel('')
ax.set_ylabel('Arrest Ratio', fontsize=17, color='black', labelpad=20)
ax2.set_ylabel('Total Crimes', fontsize=17, color='black', labelpad=20)

ax.set_facecolor('xkcd:white') # Setting face color to white

legend_colors = {'Successful Arrest Ratio': ax_color,
                 'Total Crimes': ax2_color}
add_custome_legend(color_dic=legend_colors, fontsize=15, x=0.6 ,y=1, fig=fig, handle='line')

plt.tight_layout() # Improve appearance
plt.savefig('src/success_aresst_ratio.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file

What's the Distribution of the Money-Driven Crimes by Month and Weekday?

July and August (summer months) are the most common months for crime while February and March are the least. On the other hand, Saturday and Sunday (weekends) are the least common days for crimes.

def set_bar_plot(data, x, color):
    fig, ax = plt.subplots(figsize=figsize) # Create figure and axes with default figure size
    
    base_color = sb.color_palette()[0]
    sb_barplot = sb.countplot(x=x, data=data, color=color, edgecolor=None, linewidth=0) # Create bar plot
    sb.despine(left=True, bottom=True) # Remove plot borders
    
    for p in sb_barplot.patches: # Add annotation for each patch
        sb_barplot.annotate(text       = format(int(p.get_height()), ',d'),
                            xy         = (p.get_x() + p.get_width() / 2., p.get_height()),
                            ha         = 'center', va = 'center',
                            xytext     = (0, 9),
                            textcoords = 'offset points',
                            color='black',
                            size = 15)
    
    plt.xticks(fontsize=15, color = 'black') # Increase x ticks labels font size
    plt.yticks([]), plt.xlabel(''), plt.ylabel('') # Remove ticks and label
        
    plt.title(f'Money-Driven Crimes By {x.title()}', y = title_y+.2, fontsize = title_size); # Add descriptive title
    
    plt.tight_layout() # Improve appearance
    legend = plt.legend(labels=['Theft, Burglary, Robbery and Motor Vehicle Theft Crimes'], 
                        bbox_to_anchor=(0.76, title_y+.2), fontsize = 20, handlelength=0, handletextpad=0)
    
    plt.savefig(f'src/money_crimes_{x}.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file
    
set_bar_plot(data=money_crimes, x='month', color='#4c3277')

set_bar_plot(data=money_crimes, x='weekday', color='#9e1030ff')

Where're the Most Dangerous Locations in Chicago?

Crimes are centred in Chicago in the east while it's moderate in the south and less in the north. There are at least 1,000 crime for each location with only 1 or 2 locations that exceeding 10,000 crimes.

num_of_degrees = len(gdf.severity.unique())

color_pal = ['#F60909', '#CD1CD3', '#0F7CC8', '#064085', '#7C5F0B', '#000000'] # Custom colors
mycmap = LinearSegmentedColormap.from_list('mycmap', color_pal) # Custom Colormap

color_pal.reverse() # Reverse Colormap

ax = chicago.plot(color='white', edgecolor='black', figsize=[6, 6.9])

# Assign color for each degree of severity
for n, c in zip(range(1,num_of_degrees+2), color_pal):
    if n in gdf['severity'].unique():
        gdf_current = gdf.query(f'severity == {n}')
        gdf_current.plot(ax=ax, color=c, marker='x', markersize=50)
    
plt.xticks(ticks=[]), plt.yticks(ticks=[]) # Remove x and y ticks
plt.title('The Most Dangerous Locations in Chicago between 2001 and 2017', y = title_y, fontsize=20) # Set title

sb.despine(ax=ax, left=True, bottom=True) # Remove plot borders

# Create custome colorbar
vmin, vmax = -0.5, 4.25
fig = ax.get_figure()
cax = fig.add_axes([0.97, 0.16, 0.015, 0.695])
sm = plt.cm.ScalarMappable(cmap=mycmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
# fake up the array of the scalar mappable. Urgh...
sm._A = [0, 1, 2, 3, 4]
cbar = fig.colorbar(sm, cax=cax)

cbar.set_ticks(np.arange(0, 5, 0.8))
cbar.set_ticklabels(['2K', '4K', '6K', '8K', '10K', '12K'])

cbar.outline.set_visible(False) # Remove colorbar border color

plt.savefig(f'src/top_danger_map.png', transparent=True, bbox_inches='tight', pad_inches=1); # Save as png file